Abstract
Consumer attention to food safety has increased rapidly due to animal-related diseases; therefore, it is important to identify their places of origin (POO) for safety purposes. However, only a few studies have addressed this issue and focused on machine learning-based approaches. In the present study, classification analyses were performed using a customized SNP chip for POO prediction. To accomplish this, 4,122 pigs originating from 104 farms were genotyped using the SNP chip. Several factors were considered to establish the best prediction model based on these data. We also assessed the applicability of the suggested model using a kinship coefficient-filtering approach. Our results showed that the LogitBoost-based prediction model outperformed other classifiers in terms of classification performance under most conditions. Specifically, a greater level of accuracy was observed when a higher kinship-based cutoff was employed. These results demonstrated the applicability of a machine learning-based approach using SNP chip data for practical traceability.
Highlights
Due to the occurrence of animal-related diseases such as bovine spongiform encephalopathy (BSE) and avian influenza (AI), consumer attention to food quality has increased greatly
We determined how distinguished the individual animals were according to the farms of origin using four subsets based on kinship coefficient-based filtering
We showed that the LogitBoost classifier had higher performance than other systems evaluated (KNN and support vector machine (SVM)) using various performance measures and conditions
Summary
Due to the occurrence of animal-related diseases such as bovine spongiform encephalopathy (BSE) and avian influenza (AI), consumer attention to food quality has increased greatly. Traceability is defined as a method that can guarantee the identification of animals or animal products within the food industry [1]. This system is already mandatory for most animal products in a large number of countries. Product tracking has conventionally been conducted by labeling with ear tags and tattoos [1, 2]. This technique presents several advantages, including easy application, low cost, and fast data processing, it is vulnerable to fraud or loss [1]. Genetic traceability has been proposed as an alternative to conventional
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